An infarct is an area of tissue which undergoes necrosis as a result of obstruction of local blood supply. Ischemic strokes are caused by infarction. Following an acute ischemic stroke, swift identification of the infarcted/stroke region is critical to patient outcome. It is known to perform a magnetic resonance (MR) technique known as diffusion-weighted imaging (DWI) to form an image of a region thought to include a stroke region. The DWI generates a whole volume scan consisting of a plurality of scans slices including the suspected stroke region.
Automatic computerized stroke region identification techniques have been proposed, aimed at reducing detection and analysis time compared to manual data processing by an expert, which may further be subject to human error and observer bias. Literature describes many computerized segmentation techniques aimed at reducing total time required for stroke segmentation. For example, Martel et al., MICCAI, 1679, 22-31, 1999 used a semi-automatic method to determine infarct volume by diffusion tensor-MRI. An adaptive threshold algorithm incorporating a spatial constraint was used to segment the images. Li et al., Neuroimage, 23, 1507-1518, 2004 proposed an unsupervised segmentation method using multi-scale statistical classification and partial volume voxel reclassification for diffusion tensor MR images.
To further reduce the time taken to process scan data, it is desirable to reduce the scanned volume to be studied before performing actual segmentation of the stroke regions, so as to narrow the focus of study and avoid the labour of analysing the whole volume. However, it is important that the method for reducing the volume should have high sensitivity so as not to lose information by compromising on specificity.